The present invention relates generally to a fracture pattern characterization method, and more particularly, but not by way of limitation, to a system, method, and computer program product for content-based image analytics and machine learning for fine-grained reservoir analysis, for better fracture characterization.
One of the necessary input data for reservoir/geomechanics simulation involves a complete fracture pattern characterization of each geological layer of the underlying reservoir. A fracture is a surface of discontinuity of mechanical origin. A fracture family is characterized by its attributes (e.g., dip angle, strike, length, aperture, morphology and origin). The fracture network involves the description of the fracture attributes and investigates the relationship between the different fracture families. The fracture network is characterized by the spatial properties of fractures, such as the number of fracture families, their relative fracture density, the fracture connectivity, etc.
Fracture characterization is conventionally based on experts' analysis and interpretation of results. Typically, the conventional techniques are proprietary and individual to corporations. The analysis is limited to the knowledge of a single expert, and there is no aggregation of cross-expert knowledge because of the proprietary nature of the information. Visual aspects of reservoir seismic data, such as ant-tracking results, are analyzed by human judgement only.